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import torch
from diffusers import DiffusionPipeline, StableDiffusionPipeline
from PIL import Image
import numpy as np
from typing import Optional, List, Union
import gc
# Disable torch dynamo to avoid ConstantVariable errors
torch._dynamo.config.suppress_errors = True
class OmniGenImageGenerator:
"""Image generation using OmniGen2 model"""
def __init__(self, device: str = "cuda"):
self.device = device if torch.cuda.is_available() else "cpu"
self.pipeline = None
self.model_id = "runwayml/stable-diffusion-v1-5" # Using working Stable Diffusion model
# Generation parameters
self.default_width = 512
self.default_height = 512
self.num_inference_steps = 30
self.guidance_scale = 7.5
# Memory optimization
self.enable_attention_slicing = True
self.enable_vae_slicing = True
self.enable_cpu_offload = self.device == "cuda"
def load_model(self):
"""Lazy load the image generation model"""
if self.pipeline is None:
try:
# Determine torch dtype
torch_dtype = torch.float16 if self.device == "cuda" else torch.float32
# Load pipeline with optimizations
self.pipeline = StableDiffusionPipeline.from_pretrained(
self.model_id,
torch_dtype=torch_dtype,
use_safetensors=True,
variant="fp16" if self.device == "cuda" else None
)
# Apply optimizations and device placement
if self.device == "cuda":
if self.enable_cpu_offload:
self.pipeline.enable_sequential_cpu_offload()
else:
# Safely move pipeline to CUDA
try:
self.pipeline = self.pipeline.to(self.device)
except RuntimeError as e:
if "meta tensor" in str(e):
# Handle meta tensor issue by loading with device_map
print(f"Meta tensor issue detected, using CPU fallback: {e}")
self.device = "cpu"
self.pipeline = self.pipeline.to("cpu")
else:
raise e
if self.enable_attention_slicing and hasattr(self.pipeline, 'enable_attention_slicing'):
self.pipeline.enable_attention_slicing(1)
if self.enable_vae_slicing and hasattr(self.pipeline, 'enable_vae_slicing'):
self.pipeline.enable_vae_slicing()
else:
self.pipeline = self.pipeline.to(self.device)
# Disable torch.compile to avoid dynamo issues that cause ConstantVariable errors
print("Skipping torch.compile to avoid dynamo compatibility issues")
except Exception as e:
print(f"Failed to load image generation model: {e}")
# Try fallback to stable diffusion
try:
self.model_id = "runwayml/stable-diffusion-v1-5"
self._load_fallback_model()
except:
raise
def _load_fallback_model(self):
"""Load fallback Stable Diffusion model"""
from diffusers import StableDiffusionPipeline
torch_dtype = torch.float16 if self.device == "cuda" else torch.float32
self.pipeline = StableDiffusionPipeline.from_pretrained(
self.model_id,
torch_dtype=torch_dtype,
use_safetensors=True,
trust_remote_code=True
)
if self.device == "cuda" and self.enable_cpu_offload:
self.pipeline.enable_sequential_cpu_offload()
else:
self.pipeline = self.pipeline.to(self.device)
def _truncate_prompt(self, prompt: str, max_tokens: int = 75) -> str:
"""Truncate prompt to fit CLIP token limit"""
words = prompt.split()
if len(words) <= max_tokens:
return prompt
truncated = ' '.join(words[:max_tokens])
print(f"Warning: Prompt truncated from {len(words)} to {max_tokens} words")
return truncated
def generate(self,
prompt: str,
reference_images: Optional[List[Union[str, Image.Image]]] = None,
negative_prompt: Optional[str] = None,
width: Optional[int] = None,
height: Optional[int] = None,
num_images: int = 1,
seed: Optional[int] = None) -> Union[Image.Image, List[Image.Image]]:
"""Generate monster image from prompt"""
try:
# Load model if needed
self.load_model()
# Truncate prompt to avoid CLIP token limit issues
prompt = self._truncate_prompt(prompt)
if negative_prompt:
negative_prompt = self._truncate_prompt(negative_prompt)
# Set dimensions
width = width or self.default_width
height = height or self.default_height
# Ensure dimensions are multiples of 8
width = (width // 8) * 8
height = (height // 8) * 8
# Enhance prompt for monster generation
enhanced_prompt = self._enhance_prompt(prompt)
# Default negative prompt for quality
if negative_prompt is None:
negative_prompt = (
"low quality, blurry, distorted, disfigured, "
"bad anatomy, wrong proportions, ugly, duplicate, "
"morbid, mutilated, extra limbs, malformed"
)
# Set seed for reproducibility
generator = None
if seed is not None:
generator = torch.Generator(device=self.device).manual_seed(seed)
# Generate images
with torch.no_grad():
if hasattr(self.pipeline, '__call__'):
# Standard diffusion pipeline
images = self.pipeline(
prompt=enhanced_prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=self.num_inference_steps,
guidance_scale=self.guidance_scale,
num_images_per_prompt=num_images,
generator=generator
).images
else:
# OmniGen specific generation (if different API)
images = self._omnigen_generate(
enhanced_prompt,
reference_images,
width,
height,
num_images
)
# Clean up memory
if self.device == "cuda":
torch.cuda.empty_cache()
# Return single image or list
if num_images == 1:
return images[0]
return images
except Exception as e:
print(f"Image generation error: {e}")
# Return fallback image
return self._generate_fallback_image(width, height)
def _enhance_prompt(self, base_prompt: str) -> str:
"""Enhance prompt for better monster generation"""
enhancements = [
"digital art",
"creature design",
"game character",
"detailed",
"vibrant colors",
"fantasy creature",
"high quality",
"professional artwork"
]
# Combine base prompt with enhancements
enhanced = f"{base_prompt}, {', '.join(enhancements)}"
return enhanced
def _omnigen_generate(self, prompt: str, reference_images: Optional[List],
width: int, height: int, num_images: int) -> List[Image.Image]:
"""OmniGen specific generation with multimodal inputs"""
# This would be implemented based on OmniGen's specific API
# For now, fall back to standard generation
return self.pipeline(
prompt=prompt,
width=width,
height=height,
num_images_per_prompt=num_images
).images
def _generate_fallback_image(self, width: int, height: int) -> Image.Image:
"""Generate a fallback monster image"""
# Create a simple procedural monster image
img_array = np.zeros((height, width, 3), dtype=np.uint8)
# Add some basic shapes and colors
center_x, center_y = width // 2, height // 2
radius = min(width, height) // 3
# Create circular body
y, x = np.ogrid[:height, :width]
mask = (x - center_x)**2 + (y - center_y)**2 <= radius**2
# Random monster color
color = np.random.randint(50, 200, size=3)
img_array[mask] = color
# Add eyes
eye_y = center_y - radius // 3
eye_left_x = center_x - radius // 3
eye_right_x = center_x + radius // 3
eye_radius = radius // 8
# Left eye
eye_mask = (x - eye_left_x)**2 + (y - eye_y)**2 <= eye_radius**2
img_array[eye_mask] = [255, 255, 255]
# Right eye
eye_mask = (x - eye_right_x)**2 + (y - eye_y)**2 <= eye_radius**2
img_array[eye_mask] = [255, 255, 255]
# Convert to PIL Image
return Image.fromarray(img_array)
def edit_image(self,
image: Union[str, Image.Image],
prompt: str,
mask: Optional[Union[str, Image.Image]] = None) -> Image.Image:
"""Edit existing image (for future monster customization)"""
# This would implement image editing capabilities
raise NotImplementedError("Image editing not yet implemented")
def to(self, device: str):
"""Move pipeline to specified device"""
self.device = device
if self.pipeline:
if device == "cuda" and self.enable_cpu_offload:
self.pipeline.enable_sequential_cpu_offload()
else:
self.pipeline = self.pipeline.to(device)
def __del__(self):
"""Cleanup when object is destroyed"""
if hasattr(self, 'pipeline') and self.pipeline is not None:
del self.pipeline
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache() |